LXMQ picks Privaclave AI to secure credit-card platform

LXMQ has selected Privaclave AI as a design partner on data protection for its credit-card decision platform. The work focuses on securing sensitive data in LXMQ's system ahead of a wider consumer launch.

The partnership will test Privaclave AI's technology within the LXMQ environment as the fintech develops what it describes as a Decision OS for the US credit-card market. The pilot will examine integration, architecture and potential future uses before any broader deployment.

LXMQ is building an AI engine called HALO-5, intended to connect data across card selection, rewards, repayment, fees, utilisation and credit health. The system is designed to help consumers and partners make decisions based on those combined signals rather than on historical reporting alone.

Privaclave AI offers a platform called Runtime Data Insights & Protection that detects, classifies and protects sensitive information across AI assistants, agents, large language models, APIs, applications and data pipelines. According to the company, the system assesses intent, business context, data sensitivity and policies at runtime, then applies measures such as encryption, tokenisation, masking or redaction.

The arrangement highlights growing concern in financial services over how AI systems handle highly sensitive personal and transactional data as they move from development to broader use. For fintech groups embedding AI more deeply into customer-facing products, runtime data protection is becoming a core design issue rather than a later compliance task.

That focus appears central to LXMQ's approach as it builds its platform for scale. It wants security built into the product architecture while the system is still taking shape.

"We do not want security to be something LXMQ bolts on after scale. We want trust engineered into the platform before scale," said Arun Menon, Founder, LXMQ.

Menon added that AI's role in evaluating interconnected aspects of consumer finance increases the need to protect the data flowing through the decision layer.

"When AI reasons across spending, debt, rewards, fees and credit health, protecting the data moving through that intelligence layer becomes fundamental to the product-not simply a compliance exercise," said Menon.

Runtime focus

Privaclave AI's role in the project centres on the point at which AI systems interact with live information, rather than only on perimeter security or user access controls. That reflects a broader industry debate over whether existing security models are sufficient when AI tools can access, combine and act on enterprise data in real time.

Sid Dutta, Founder and Chief Executive Officer of Privaclave AI, said this requires systems that can interpret what is happening as data is being used. "AI security requires more than visibility, alerts, policy-based blocking or identity-based access controls," said Sid Dutta, Founder and Chief Executive Officer, Privaclave AI.

He described runtime analysis as the key differentiator in securing AI workflows. "Secure AI depends on understanding intent, context and data sensitivity at runtime-and automatically applying protection as AI systems interact with enterprise information. LXMQ is building intelligence, privacy and security together from day one," said Dutta.

Product development

The design-partner model also suggests the relationship extends beyond a standard vendor contract. In a separate public post, Menon described it as a strategic partnership that would allow both companies to learn together, influence product development and explore how trusted AI infrastructure could support financial services.

For LXMQ, the collaboration offers a way to test how sensitive information can be handled as its platform brings together data on transactions, annual percentage rates, due dates, statement cycles and user goals. For Privaclave AI, the pilot provides a financial services setting in which to assess how its protection system performs when embedded in a decisioning product still under development.

Financial institutions and fintech groups are under pressure to show that AI systems can produce useful outputs without exposing personal data or creating new operational risks. This is especially acute in credit-card services, where repayment history, spending patterns, fees, utilisation and account behaviour can all feed into models and recommendations.

The companies will also explore joint innovation around trusted AI infrastructure for financial services. The immediate task, however, is the pilot inside LXMQ's environment, where both sides will test how runtime protections fit into the architecture before any larger rollout.